Take another look at the hardhat and safety glasses on the food
safety inspector. Mounted on the hardhat is a small camera and
a flashlight that gives off specially fi ltered light.
The safety glasses are actually a wearable miniature computer
monitor that displays data from a miniature computer on the inspector’s
belt. The data tells the inspector whether there is any fecal matter
on the processing equipment.

Another inspector might be looking through what at first looks
like a pair of ordinary binoculars. But these binocular lenses filter
special bands of light to check for disease, defects, or fecal matter
on
the meat, produce, or equipment.
There’s also a hand-held device that shines fi ltered light to
do a
sanitation check of the processing plant. The device has a camera
that sends images to another eyewear-mounted computer display.
White specks on the image reveal fecal matter.

Although these gadgets sound like something dreamed up by
James Bond’s gadget man, cutting-edge prototypes like this actually
exist in an Agricultural Research Service lab in Beltsville, Maryland.
At the Instrumentation and Sensing Laboratory (ISL), a team of
scientists—led by Yud-Ren Chen and including biophysicist Moon
Kim, agricultural engineer Kuanglin Chao, and visiting scientists
from around the globe—design the portable inspection devices.

Chen, Chao, and visiting scientist Chun-Chieh Yang have fi nished
work on a high-speed on-line imaging system for chicken
inspection. They are turning over a prototype to industry as part of
a
cooperative research and development agreement with Stork-Gamco
of Gainesville, Georgia, a major manufacturer of chicken-processing
equipment. Chen and Kim and biomedical engineer Alan Lefcourt
are working on a similar system for inspecting fruits and vegetables.
Because all these systems use optically filtered light and optoelectronics
to “see,” they are called “machine vision” or “optical
sensing” systems. At the heart of these machine vision systems is
a
digital multispectral camera that can take photos at different wavelengths
simultaneously and can even detect light invisible to the
naked eye. The systems include the latest, fastest cameras of this
type. All the systems rely on two or three wavelengths chosen to do
the best job of seeing special features.

Fully funded by ARS—with additional funds from industry—Chen’s
team works with both industry and universities, such as the University
of Kentucky and the nearby University of Maryland at
College Park.
Sensing Remotely, Close Up
Machine vision using multiple images at selected wavelengths is
also being developed for use in remote sensing of Earth by satellite
imagery. But its potential for use in monitoring food safety and quality
should be even greater, since the sensors are only inches away
from the target object, and there is a wider range of applications.
The basic idea of machine vision is to supplement human inspectors
with instruments that shine light on every single fruit, vegetable,
meat, or poultry product as it speeds by on the processing line faster
than ever. Typical lines today can process about 360 fruits per minute
or up to 180 poultry carcasses per minute, for example.
The system developed by Chen’s team spots almost all biological
conditions that cause inspectors to take a second look at chicken
carcasses, such as signs of diseases that pose food safety risks or
otherwise mar a chicken’s consumer appeal.

Chen’s team is now focusing its attention on apples, developing
a system that could be used for other fresh produce as well. It can
detect contaminants on the apple surface, such as fecal matter.
Stephen Delwiche, an agricultural engineer at the ISL, works
with colleagues at the ARS Grain Marketing and Production
Research Center in Manhattan, Kansas, on high-speed optical
inspection of wheat and other grains. He uses near-infrared reflected
light to detect proteins in wheat as well as scab and other molds.

Quality More Than Skin Deep
In East Lansing, Michigan, ARS engineer Renfu Lu, who originally
worked with Chen at Beltsville, is leading a research team that
uses similar optical technologies to judge taste and other quality
aspects of produce. He has worked with apples, peaches, and cherries
using a prototype optical detector he and colleagues devised that
uses laser beams to detect fruit sweetness and fi rmness.

The team consists of research associates Diwan Ariana and
Hyunkwon Noh, visiting assistant professor Yankun Peng, engineering
technician Benjamin Bailey, and a Ph.D. graduate student,
Jianwei Qin, of Michigan State University (MSU) at East Lansing.
Lu and colleagues are refining the mathematical equations and
the imaging sensor used by the prototype to judge the internal quality
of fruit.

“We should have an improved machine vision prototype
for ‘tasting’ apples and other fruit very soon,” Lu
says. He is now expanding the machine vision inspection system to
pickling cucumbers, inspecting for bruises and other defects as well
as internal quality factors—such as fi rmness, dry matter content,
and
color.

“
We want to select the best cucumbers—those that are fi rm, have
a fresh green color, and aren’t too soggy,” says Lu.

In terms of acreage planted and crop yields, Michigan is the top
state for pickling cucumbers by far, with a value of $30 million a
year. And at $150 million a year, pickling cucumbers are also one of
the most valuable vegetables in the United States, competing only
with tomatoes and sweet corn.

Lu’s research program is also fully funded by USDA, with additional
funds from industry, and it partners with MSU’s Biosystems
and Agricultural Engineering Department to address priority needs
of the produce industry in Michigan and the nation.
When commercialized, Lu’s optical sensors would be used by the
fruit industry to sort fruits and vegetables just after they had been
picked and again on the processing line. The equipment would likely
be blended into existing industry sensors that nondestructively assess
superfi cial visual traits, including size, color, and bruising.

Editor’s note: This research is part of Food Safety (#108) and
Quality and
Utilization of Agricultural Products (#306), two ARS National Programs
described on
the World Wide Web at: www.nps.ars.usda.gov.
Yud-Ren Chen is with the USDA-ARS Instrumentation and Sensing Laboratory,
Bldg. 303, 10300 Baltimore Ave., Beltsville, MD 20705-2350; phone (301)
504-8450, fax (301) 504-9466.
Renfu Lu is in the USDA-ARS Sugarbeet and Bean Research Unit, Michigan
State University, East Lansing, MI 48824; “Machine’s Eye
View of Poultry and
Produce” is reprinted from the January 2007 issue of Agricultural
Research
magazine.